Surveillance systems are used in general to monitor buildings, public places, traffic and the like. These systems conventionally comprise a plurality of surveillance cameras producing a large amount of video data. The video data is viewed on-line or recorded and searched through off-line. Especially in the latter case there is a strong need to improve the search speed due to the large amount of the video data.
In the field of moving-objects-tracking it is known to use video content analysis algorithms (VCA) in order to support the search or the retrieval of video data, whereby in a first step the video camera images are segmented into static background and moving objects. In a further step these objects are tracked over time and the locations of the objects in each frame are extracted. The set of locations of each object over the life-time of the object is converted into a trajectory for each object. These trajectories can be stored in a database and used to search through the recorded video camera images.
The document U.S. Pat. No. 6,587,574 B1 discloses a system and a method for representing trajectories of moving objects for content-based indexing and retrieval of visual animated data. The method comprises the steps as elucidated above, whereby a descriptive data structure is generated on the basis of the extracted trajectories and whereby the descriptive data structure comprises at least trajectory data representative for the position, velocity and/or acceleration of the moving object.
The scientific paper from Dimitrios Makris and Tim Ellis with the title “path detection in video surveillance” in “Image and Vision Computing” 20 (2002), pp. 895-903 is addressed to the problem of automatically extracting frequently used pedestrian pathways from video sequences of natural outdoor scenes. This paper discloses to learn path models from the accumulation of trajectory data over long time periods. As an application it is proposed to label common paths, to log moving objects (pedestrians) in respect to the common paths and to predict a pedestrian's location many time steps ahead or to recognise an unusual behaviour of the pedestrian.